Businesses commonly advertise through many different media in order to attract customers. Ads reach customers via a variety of modalities including, for example, online websites, printed publications, radio and television broadcasts, direct mailings, emails, and other mediums in which customers are likely to view the ads.
Increasingly, businesses are focusing their advertising efforts on online or Internet ads. As online advertising spending continues to climb, there is a strong interest in being able to attribute customer actions to a particular advertisement or marketing channel. Actions that occur during a visit to a website, such as for example submitting an inquiry form or downloading a file, can be tracked and associated with other visitor information including, for example, a visitor source channel or a searched keyword term. However, offline actions, such as phone calls, cannot typically be tracked in the same manner.
Current methods for associating phone calls with visitor data involve assigning a unique phone number to each ad created. In that case, if a caller contacts a business via a particular phone number, it can be assumed that the caller viewed the ad that displayed that phone number. This method of associating a phone call with a website visit is inefficient, because it requires a business to purchase a large number of phone numbers such that each ad can be associated with its own phone number.
Another alternative involves dynamically assigning forwarding phone numbers for each visit and/or page-view. This method creates increasing uncertainty when the number of visitors and page-views increases and/or when tracked details are more refined, e.g., tracking keywords vs. merely tracking all visits from organic searches. This is because a finite number of phone numbers are spread, and recycled, through an increasing number of possible visit information combinations.
For instance, if an interior design company merely tracked visitor source, all visits from organic searches would be assigned the same phone number. However, to track each keyword searched, a unique phone number would be assigned to each phrase. Thus, “sofa upholstery” and “couch upholstery” would require different phone numbers. Since the business owns a finite pool of phone numbers, which are dynamically assigned and reused, this increases the likelihood that identical phone numbers would be reused frequently enough to render the system error-prone.
Additionally, if the phone number is displayed on multiple areas on the page, this method would not be capable of disclosing which phone number a visitor looked or clicked at to call. Such data would be important for a business attempting to understand which area of a web page attracts the attention of visitors. To accomplish this with the current method, a business would need to feature different phone numbers on the same page, which would be impractical.
Furthermore, the current method for associating a phone call with a website visit uses analytics collected by the call tracking system independently and does not connect the tracking to the website's existing web analytics system. This approach limits the availability of data, since the website's analytics system specializes in collecting a wide array of visitor data. By connecting phone calls and call information to the analytics system, the disclosed method unleashes a stream of opportunities for improved analytics and operations of a business.
Current tracking methods also interfere with the branding efforts of businesses, as it may be desirable for customers to associate a particular business with a particular or memorable phone number.
Furthermore, traditional call tracking methods involve passing a call through the mentioned tracking numbers, which incurs charges based on the usage of the network associated with these numbers (for instance, per minute of talking time). For a business with substantial call volume, the cost associated with such a setup is significant.
Lastly, the transition to offline, via either a phone call or form submission, introduces challenges in optimizing online advertising. As businesses are increasingly interested in improving ad spend ROI, there is growing interest to target online advertising by intercepting additional data about a user, such as profile and activity. Thus, tracking a user's web activity and using the data to predict interest and guide advertising display decisions is commonplace. However, the utilized data is restricted to online browsing behavior. Since phone calls and form submissions generate offline activity, and given the sheer volume of activity, the ability to utilize this information to target online advertising is extremely valuable. The present disclosure enables information other than online browsing behavior to be utilized in the same manner as online browsing behavior in targeting online advertising.